H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 43 Citations 7,661 137 World Ranking 3969 National Ranking 370

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Guiguang Ding mainly investigates Artificial intelligence, Pattern recognition, Machine learning, Feature extraction and Contextual image classification. Guiguang Ding integrates Artificial intelligence and Space in his studies. His Pattern recognition research incorporates themes from Sparse matrix and Image, Image retrieval.

Guiguang Ding has included themes like Transfer of learning, Semantics, Visualization and Regularization in his Feature extraction study. Guiguang Ding combines subjects such as Classifier, Feature learning and Dimensionality reduction with his study of Transfer of learning. His work focuses on many connections between Visualization and other disciplines, such as Hypergraph, that overlap with his field of interest in Data mining.

His most cited work include:

  • Transfer Feature Learning with Joint Distribution Adaptation (780 citations)
  • Transfer Joint Matching for Unsupervised Domain Adaptation (384 citations)
  • Collective Matrix Factorization Hashing for Multimodal Data (325 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Convolutional neural network and Image. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Computer vision. His studies deal with areas such as Contextual image classification and Margin as well as Pattern recognition.

The various areas that Guiguang Ding examines in his Machine learning study include Structure and Set. The Image study combines topics in areas such as Feature and Natural language processing. His research integrates issues of Binary code and Visualization in his study of Feature extraction.

He most often published in these fields:

  • Artificial intelligence (64.81%)
  • Pattern recognition (29.63%)
  • Machine learning (22.84%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (64.81%)
  • Convolutional neural network (17.28%)
  • Image (16.67%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Convolutional neural network, Image, Pattern recognition and Feature extraction. As part of his studies on Artificial intelligence, Guiguang Ding often connects relevant subjects like Natural language processing. His biological study spans a wide range of topics, including Contextual image classification, Algorithm and Feature vector.

The Image study combines topics in areas such as Transfer of learning and Discriminative model. His study in the fields of Classifier under the domain of Pattern recognition overlaps with other disciplines such as Adaptation. His research investigates the connection with Feature extraction and areas like Feature which intersect with concerns in Margin and Categorical variable.

Between 2019 and 2021, his most popular works were:

  • Discrete Probability Distribution Prediction of Image Emotions with Shared Sparse Learning (29 citations)
  • IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval (18 citations)
  • Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification (16 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Artificial intelligence, Feature extraction, Deep learning, Image and Convolutional neural network are his primary areas of study. His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning. Guiguang Ding works mostly in the field of Feature extraction, limiting it down to concerns involving Feature and, occasionally, Categorical variable and Discriminative model.

His work deals with themes such as Benchmark and Pattern recognition, which intersect with Deep learning. He interconnects Margin and Feature based in the investigation of issues within Pattern recognition. The various areas that he examines in his Convolutional neural network study include Hyperparameter, Topology and Parallel computing.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Transfer Feature Learning with Joint Distribution Adaptation

Mingsheng Long;Jianmin Wang;Guiguang Ding;Jiaguang Sun.
international conference on computer vision (2013)

694 Citations

Transfer Joint Matching for Unsupervised Domain Adaptation

Mingsheng Long;Jianmin Wang;Guiguang Ding;Jiaguang Sun.
computer vision and pattern recognition (2014)

485 Citations

Collective Matrix Factorization Hashing for Multimodal Data

Guiguang Ding;Yuchen Guo;Jile Zhou.
computer vision and pattern recognition (2014)

444 Citations

Adaptation Regularization: A General Framework for Transfer Learning

Mingsheng Long;Jianmin Wang;Guiguang Ding;Sinno Jialin Pan.
IEEE Transactions on Knowledge and Data Engineering (2014)

396 Citations

Semantics-preserving hashing for cross-view retrieval

Zijia Lin;Guiguang Ding;Mingqing Hu;Jianmin Wang.
computer vision and pattern recognition (2015)

315 Citations

Latent semantic sparse hashing for cross-modal similarity search

Jile Zhou;Guiguang Ding;Yuchen Guo.
international acm sigir conference on research and development in information retrieval (2014)

310 Citations

Transfer Sparse Coding for Robust Image Representation

Mingsheng Long;Guiguang Ding;Jianmin Wang;Jiaguang Sun.
computer vision and pattern recognition (2013)

188 Citations

Transfer learning with graph co-regularization

Mingsheng Long;Jianmin Wang;Guiguang Ding;Dou Shen.
national conference on artificial intelligence (2012)

175 Citations

Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression

Sicheng Zhao;Hongxun Yao;Yue Gao;Rongrong Ji.
IEEE Transactions on Multimedia (2017)

163 Citations

Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing

Guiguang Ding;Yuchen Guo;Jile Zhou;Yue Gao.
IEEE Transactions on Image Processing (2016)

135 Citations

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Best Scientists Citing Guiguang Ding

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 64

Fumin Shen

Fumin Shen

University of Electronic Science and Technology of China

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Dacheng Tao

Dacheng Tao

University of Sydney

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Yun Fu

Yun Fu

Northeastern University

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Zhengming Ding

Zhengming Ding

Tulane University

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Xuelong Li

Xuelong Li

Northwestern Polytechnical University

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Heng Tao Shen

Heng Tao Shen

University of Electronic Science and Technology of China

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Zi Huang

Zi Huang

University of Queensland

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Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

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Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 29

Sicheng Zhao

Sicheng Zhao

University of California, Berkeley

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Jungong Han

Jungong Han

Aberystwyth University

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Dongrui Wu

Dongrui Wu

Huazhong University of Science and Technology

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Rongrong Ji

Rongrong Ji

Xiamen University

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Qiang Yang

Qiang Yang

Hong Kong University of Science and Technology

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Yang Yang

Yang Yang

University of Electronic Science and Technology of China

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